Wine Quality by Pratyush Kumar

Description

In this project, we will explore a data set on red wines quality. Our main objective is to explore the chemical properties influences the quality of red wines. This tidy data set contains 1,599 red wines with 11 variables on the chemical properties of the wine. The data set is available here and information about the data set is available here.

Statistical Summary

Structure of the dataset

## 'data.frame':    1599 obs. of  13 variables:
##  $ X                   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
##  $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
##  $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
##  $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
##  $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
##  $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...
##  $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...
##  $ density             : num  0.998 0.997 0.997 0.998 0.998 ...
##  $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
##  $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
##  $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
##  $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...

There are 1,599 observation with total 13 variables.
X is an unique identifier.
All the variables in the dataset have numeric values except X and quality which are integer.

Statistical summary of the variables in the dataset

##        X          fixed.acidity   volatile.acidity  citric.acid   
##  Min.   :   1.0   Min.   : 4.60   Min.   :0.1200   Min.   :0.000  
##  1st Qu.: 400.5   1st Qu.: 7.10   1st Qu.:0.3900   1st Qu.:0.090  
##  Median : 800.0   Median : 7.90   Median :0.5200   Median :0.260  
##  Mean   : 800.0   Mean   : 8.32   Mean   :0.5278   Mean   :0.271  
##  3rd Qu.:1199.5   3rd Qu.: 9.20   3rd Qu.:0.6400   3rd Qu.:0.420  
##  Max.   :1599.0   Max.   :15.90   Max.   :1.5800   Max.   :1.000  
##  residual.sugar     chlorides       free.sulfur.dioxide
##  Min.   : 0.900   Min.   :0.01200   Min.   : 1.00      
##  1st Qu.: 1.900   1st Qu.:0.07000   1st Qu.: 7.00      
##  Median : 2.200   Median :0.07900   Median :14.00      
##  Mean   : 2.539   Mean   :0.08747   Mean   :15.87      
##  3rd Qu.: 2.600   3rd Qu.:0.09000   3rd Qu.:21.00      
##  Max.   :15.500   Max.   :0.61100   Max.   :72.00      
##  total.sulfur.dioxide    density             pH          sulphates     
##  Min.   :  6.00       Min.   :0.9901   Min.   :2.740   Min.   :0.3300  
##  1st Qu.: 22.00       1st Qu.:0.9956   1st Qu.:3.210   1st Qu.:0.5500  
##  Median : 38.00       Median :0.9968   Median :3.310   Median :0.6200  
##  Mean   : 46.47       Mean   :0.9967   Mean   :3.311   Mean   :0.6581  
##  3rd Qu.: 62.00       3rd Qu.:0.9978   3rd Qu.:3.400   3rd Qu.:0.7300  
##  Max.   :289.00       Max.   :1.0037   Max.   :4.010   Max.   :2.0000  
##     alcohol         quality     
##  Min.   : 8.40   Min.   :3.000  
##  1st Qu.: 9.50   1st Qu.:5.000  
##  Median :10.20   Median :6.000  
##  Mean   :10.42   Mean   :5.636  
##  3rd Qu.:11.10   3rd Qu.:6.000  
##  Max.   :14.90   Max.   :8.000

As we are primarily interested in quality of the red wines, let’s see some basic statistics about it

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.000   5.000   6.000   5.636   6.000   8.000

We’ve found that the quality lies in the range of [3, 8] with mean is equal to 5.636.

Univariate Plots Section

Univariate Analysis

Quality of wine

As we can see the entire wine quality are in the range of 3 to 8 with the most common values are 5 and 6 and the least common values are 3, 4, 7, and 8. So, we create another variable rating with rate given below.

0 - 4 : poor
5 - 6 : good
7 - 10 : ideal

##  poor  good ideal 
##    63  1319   217

Calculate total acidity

To calculate sum of all acids in the red wines, we create new variable total.acidity.

## [1]  8.10  8.68  8.60 12.04  8.10  8.06

Distribution and Outliers

fixed.acidity, volatile.acidity, sulfur.dioxide, sulphated and alcohol are appeared to be long tailed.
density and pH are normally distributed with few outliers.
residual.sugar and chlorides have extreme outliers.
citric.acid contains large number of zero values.

Plot base 10 logarithmic scale of the long tailed distribution

Taking log_10, we can see that fixed.acidity, volatile.acidity, and sulphates are normally distributed, with some few outliers.

Find number of zeroes in `citric.acid`

## [1] 132

We found that 132 observations have zero values.

Find some patterns in `residual.sugar` and `chlorides` after removing some extreme outliers

After removing some extreme outliers, we see chlorides are now normally distributed
Since residual.sugar comes in wide range as, it’ is’s rare to find wines with less than 1 gm/liter and wines with greater than 45 gm/liter are considered sweet, so the range of 1 - 4 as we found in the plot are ok with some outliers.

Some Questions

What is the structure of your dataset?

## 'data.frame':    1599 obs. of  15 variables:
##  $ X                   : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
##  $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
##  $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
##  $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
##  $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
##  $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...
##  $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...
##  $ density             : num  0.998 0.997 0.997 0.998 0.998 ...
##  $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
##  $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
##  $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
##  $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...
##  $ rating              : Ord.factor w/ 3 levels "poor"<"good"<..: 2 2 2 2 2 2 2 3 3 2 ...
##  $ total.acidity       : num  8.1 8.68 8.6 12.04 8.1 ...

What is/are the main feature(s) of interest in your dataset?

As our main objective is to conclude quality. So, it’s the main feature.

What other features in the dataset do you think will help support your investigation into your feature(s) of interest?

density and pH are also normally distributed as our new variable rating. So, these two can help support our analysis.

Did you create any new variables from existing variables in the dataset?

I created an ordered factor rating, level each variables as ‘poor’, ‘good’ and ‘ideal’.
And, total.acidity to calculate sum of all acids.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

residual.sugar and chlorides contains many outliers but after doing some operations, chlorides get into normal distribution
citric.acid have very large number of zero values but after reading documentation it’s fine as it found in small quantities.

Bivariate Plots Section

Bivariate Analysis

Boxplot of `quality`

Boxplot of `rating`

poor rating seems to have following trends:

lower fixed.acidity, higher volatile.acidity and lower citric.acid
lower sulfur.dioxide and sulphates
higher pH and high ```density

good rating seems to have following trends:

low fixed.acidity and volatile.acidity
higher sulfur.dioxide
low pH and higher density

ideal rating seems to have following trends:

higher fixed.acidity , lower volatile.acidity and higher citric.acid
low sulfur.dioxide and higher sulphates
lower pH and density

Correlation of variables against quality

##        fixed.acidity     volatile.acidity          citric.acid 
##           0.12405165          -0.39055778           0.22637251 
##       residual.sugar            chlorides  free.sulfur.dioxide 
##           0.01373164          -0.12890656          -0.05065606 
## total.sulfur.dioxide              density                   pH 
##          -0.18510029          -0.17491923          -0.05773139 
##            sulphates              alcohol        total.acidity 
##           0.25139708           0.47616632           0.10375373

Following variables show strong correlations with quality

alcohol
sulphates
citric.acid
fixed.acidity

Let’s see some relationships between some variable and total.acidity

We see that there is approx. linear relationship between density and log10(total.acidity), and pH and log10(total.acidity).

Multivariate Plots Section

Multivariate Analysis

I plot graph between four variables citric.acid, fixed.acidity, sulphates and alcohol which shown high correlations with quality and faceted them with rating. I conclude that higher citric.acid and lower fixed.acidity yields better wines. Better wines also have higher alcohol and sulphates and lower pH.

Final Plots and Summary

Plot One : Effect of alcohol on wine quality

Description One

As alcohol is highly correlated with the quality, it is better to see its pattern with varying rating. From the above plot, it clearly shows higher % of alcohol yields better wine.

Plot Two : Effect of acids on wine quality

Description Two

As more the acidic better is the wine. It would be better to see which acids have more impact on wine quality. Above plot shows, fixed.acidity and citric.acid have highly correlated with quality but volatile.acidity has negative impact on quality.

Plot Three : Effect of pH on wine quality

Description Three

From the above plot, it shows there is linear relationships between pH and total.acidity. This means lower the pH, more acidic the wines. This also substantiate that higher the acidic tends to better the wine.

Plot Four : Ideal vs poor wine

Description Four

It would be great to see the real pattern between good and bad wines. Above plot differentiate between good and bad wines. It shows higher the % of alcohol and higher the sulphates give better wines.

Reflection

After this EDA, I can conclude that the major factors for better wine quality is alcohol, acidity and sulphates. These features must be in required content otherwise negative impact will effect the wine quality. Also, we can’t be totally sure about quality index also it has been taken some experts. We’ve also concluded that there is linear relationship between pH and quality with negative slope.

One thing that is still unclear is the amount of residual.sugar. It contains many outliers, also after doing some operation we get its common range from 1 to 4. But we can’t find its amount for ideal wine quality. I think more future research need to be done to find its ideal quantity.